Project Source Code

Loading Libraries:

# Load required libraries

suppressWarnings({
 library(tidyverse)
library(ggplot2)
library(plotly)  
library(maps)
library(leaflet)
library(readr)

library(plotly)
library(dplyr)
library(viridis) 
library(rnaturalearth)  # For loading world map data


})

Average Suicidal Rates By Country from 1950 to 2022:

# Load the data (change the path to where your file is stored)
suicide_data <- read.csv("/Users/kiran/Documents/GMU/STAT 515/Mid Project/suicide-rates-all.csv")

# Summarize the data to get the average suicide rate for each country
average_suicide_rate <- suicide_data %>%
  group_by(Country) %>%
  summarise(Average_Suicide_Rate = mean(suscide.rate, na.rm = TRUE), .groups = "drop")  # Add .groups = "drop" to avoid warnings

# Load world map data
world <- ne_countries(scale = "medium", returnclass = "sf")

# Merge world map data with average suicide rates
world_data <- merge(world, average_suicide_rate, by.x = "name", by.y = "Country", all.x = TRUE)

# Create color palette for suicide rates
pal <- colorNumeric(palette = "YlOrRd", domain = world_data$Average_Suicide_Rate, na.color = "transparent")

# Create interactive leaflet map
leaflet(world_data) %>%
  addTiles() %>%
  addPolygons(
    fillColor = ~pal(Average_Suicide_Rate),
    weight = 1,
    opacity = 1,
    color = "white",
    dashArray = "3",
    fillOpacity = 0.7,
    highlight = highlightOptions(
      weight = 5,
      color = "#666",
      dashArray = "",
      fillOpacity = 0.7,
      bringToFront = TRUE
    ),
    label = ~paste(name, "Average Suicide Rate: ", round(Average_Suicide_Rate, 2)),
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto"
    )
  ) %>%
  addLegend(pal = pal, values = ~Average_Suicide_Rate, opacity = 0.7, title = "Average Suicide Rate per 100,000", position = "bottomright") %>%
  addControl("<strong>Average Suicide Rates by Country of all the ages over the years 1950 to 2022</strong>", position = "topright", 
             className = "map-title")  # Add title to the map

Average Suicide Rates of all the ages by Country for the top rated year: 1982

# Load the data (change the path to where your file is stored)
suicide_data <- read.csv("/Users/kiran/Documents/GMU/STAT 515/Mid Project/suicide-rates-all.csv")

# Calculate the overall average suicide rate for each year across all countries
yearly_avg_suicide_rate <- suicide_data %>%
  group_by(Year) %>%
  summarise(Average_Suicide_Rate = mean(suscide.rate, na.rm = TRUE))

# Find the year with the highest overall average suicide rate
top_year <- yearly_avg_suicide_rate %>%
  filter(Average_Suicide_Rate == max(Average_Suicide_Rate)) %>%
  pull(Year)

# Filter the data to include only the data for the top year
top_year_data <- suicide_data %>%
  filter(Year == top_year)

# Load world map data
world <- ne_countries(scale = "medium", returnclass = "sf")

# Merge world map data with suicide rate data for the top year
world_data <- merge(world, top_year_data, by.x = "name", by.y = "Country", all.x = TRUE)

# Create color palette for the suicide rates in the top year
pal <- colorNumeric(palette = "YlOrRd", domain = world_data$suscide.rate, na.color = "transparent")

# Create interactive leaflet map
map <- leaflet(world_data) %>%
  addTiles() %>%
  addPolygons(
    fillColor = ~pal(suscide.rate),
    weight = 1,
    opacity = 1,
    color = "white",
    dashArray = "3",
    fillOpacity = 0.7,
    highlight = highlightOptions(
      weight = 5,
      color = "#666",
      dashArray = "",
      fillOpacity = 0.7,
      bringToFront = TRUE
    ),
    label = ~paste(name, "Suicide Rate: ", round(suscide.rate, 2)),
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto"
    )
  ) %>%
  addLegend(pal = pal, values = ~suscide.rate, opacity = 0.7, title = paste("Suicide Rate per 100,000 in ", top_year), position = "bottomright")%>%
addControl("<strong>Average Suicide Rates of all the ages by Country for the top rated year: 1982</strong>", position = "topright", 
             className = "map-title")  # Add title to the map

# Show the map
map

Average Suicide Rates by Year all over the World:

# Read the data
data <- read_csv(
  "/Users/kiran/Documents/GMU/STAT 515/Mid Project/suicide-rates-all.csv",
  col_types = cols(
    Country = col_character(),
    Code = col_character(),
    Year = col_double(),
    `suscide-rate` = col_double()
  )
)

# Step 1: Calculate the average suicide rate for each year
avg_suicide_by_year <- data %>%
  group_by(Year) %>%
  summarise(Average_suicide_rate = round(mean(`suscide-rate`, na.rm = TRUE), 2), .groups = "drop")

# Step 2: Create the frequency polygon
frequency_polygon <- ggplot(avg_suicide_by_year, aes(x = Year, y = Average_suicide_rate)) +
  geom_line(stat = "identity", color = "blue", size = 1) +  # Line to connect data points
  geom_point(color = "red") +  # Points for each year
  labs(title = "Suicide Rates trend of all the ages and Years all over the world", 
       x = "Year", 
       y = "Average Suicide Rate per 100,000") +
  
  theme(
    plot.title = element_text(face = "bold"), 
    axis.title.x = element_text(face = "bold"), 
    axis.title.y = element_text(face = "bold")
  )

# Convert the frequency polygon to an interactive plot
interactive_frequency_polygon <- ggplotly(frequency_polygon)

# Show the interactive frequency polygon
interactive_frequency_polygon

Top 5 Years with Highest Suicide Rates in Top 5 Countries:

# Load the data (update the path to your CSV file accordingly)
suicide_data <- read.csv("/Users/kiran/Documents/GMU/STAT 515/Mid Project/suicide-rates-all.csv")

# Calculate the average suicide rate per country and filter top 5 countries
top_5_countries <- suicide_data %>%
  group_by(Country) %>%
  summarise(Average_Suicide_Rate = mean(suscide.rate, na.rm = TRUE)) %>%
  top_n(5, wt = Average_Suicide_Rate)

# Find the top 5 years for each of the top 5 countries
top_5_years_per_country <- suicide_data %>%
  filter(Country %in% top_5_countries$Country) %>%
  group_by(Country, Year) %>%
  summarise(Suicide_Rate = mean(suscide.rate, na.rm = TRUE)) %>%
  arrange(Country, desc(Suicide_Rate)) %>%
  group_by(Country) %>%
  slice_max(Suicide_Rate, n = 5)

# Create a horizontal bar plot with increased bar size
p_bar <- ggplot(top_5_years_per_country, aes(x = reorder(Country, Suicide_Rate), y = Suicide_Rate, fill = Year, text = paste("Country:", Country, "<br>Year:", Year, "<br>Suicide Rate:", round(Suicide_Rate, 2)))) +
  geom_bar(stat = "identity", position = position_dodge(), width = 0.9) +  # Adjust width to increase bar size
  scale_fill_gradient(low = "#FF9999", high = "#CC0000") +  # Light to dark red gradient
  labs(title = "Top 5 Countries with Highest Suicide Rates and Their Top 5 Years",
       x = "Suicide Rate",
       y = "Country") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5),
        legend.position = "none") +
  coord_flip() +  # Flip the axes to create horizontal bars
  
  # Add year labels to the bars
  geom_text(aes(label = Year), 
            position = position_dodge(width = 0.9),  # Ensure labels match bar positions
            hjust = -0.2,   # Adjust horizontal position of text
            size = 3,       # Set a smaller text size
            fontface = "bold")       # Set a smaller text size

# Convert ggplot to interactive plotly plot
interactive_bar_plot <- ggplotly(p_bar, tooltip = "text")

# Display the interactive plot
interactive_bar_plot

Average Suicide Rates for Ages 15-19 over the years:

# Load the CSV file
children_data <- read.csv("/Users/kiran/Downloads/suicide-rates-by-age.csv")

# Step 1: Rename columns to appropriate labels
colnames(children_data) <- c("Country", "Code", "Year", 
                              "Death_rate_15_19", "Death_rate_20_24", "Death_rate_25_29", 
                              "Death_rate_30_34", "Death_rate_35_39", "Death_rate_40_44", 
                              "Death_rate_45_49", "Death_rate_50_54", "Death_rate_55_59", 
                              "Death_rate_60_64", "Death_rate_65_69", "Death_rate_70_74", 
                              "Death_rate_75_79", "Death_rate_80_84", "Death_rate_over_85")

# Step 2: Calculate the average suicide rate for children (using 'Death_rate_15_19' as an example)
avg_suicide_rate <- children_data %>%
  group_by(Country) %>%
  summarise(AverageRate = round(mean(Death_rate_15_19, na.rm = TRUE),2))

# Step 3: Load map data
world_map <- map_data("world")

# Step 4: Merge average suicide rates with the map data
map_data <- world_map %>%
  left_join(avg_suicide_rate, by = c("region" = "Country")) # Ensure to match the correct column names

# Step 5: Create the static map using ggplot2
gg <- ggplot(data = map_data, aes(x = long, y = lat, group = group, fill = AverageRate)) +
  geom_polygon(color = "black", aes(text = paste("Country:", region, "<br>Average Suicide Rate (Ages 15-19):", round(AverageRate, 2)))) +
  scale_fill_gradient(low = "lightblue", high = "red", na.value = "grey50", 
                      name = "Average Suicide Rate\n(Ages 15-19)") +
  labs(title = "Average Suicide Rate of Children by Country (Ages 15-19)",
       x = "Longitude", 
       y = "Latitude") +
  theme_minimal() +
  theme(legend.position = "right")

# Step 6: Convert ggplot to an interactive plotly object
interactive_map <- ggplotly(gg, tooltip = "text")

# Step 7: Display the interactive map
interactive_map

Average Suicide Rate for the Top 20 Nations in the 15-19 Age Group (Across All Years):

# Load necessary libraries
library(ggplot2)
library(dplyr)
library(plotly)

# Load the data
data <- read.csv("/Users/kiran/Downloads/suicide-rates-by-age.csv")

# Clean column names for easier access
colnames(data) <- c("Entity", "Code", "Year", 
                    "Death_rate_15_19", "Death_rate_20_24", "Death_rate_25_29", 
                    "Death_rate_30_34", "Death_rate_35_39", "Death_rate_40_44", 
                    "Death_rate_45_49", "Death_rate_50_54", "Death_rate_55_59", 
                    "Death_rate_60_64", "Death_rate_65_69", "Death_rate_70_74", 
                    "Death_rate_75_79", "Death_rate_80_84", "Death_rate_over_85")

# Filter the data for the 15-19 age group and remove missing values
data_filtered <- data %>%
  filter(!is.na(Death_rate_15_19))

# Calculate the average suicide rate for each country across all years
avg_suicide_rate <- data_filtered %>%
  group_by(Entity) %>%
  summarise(suscide_Rate = round(mean(Death_rate_15_19, na.rm = TRUE), 2)) %>%
  arrange(desc(suscide_Rate)) %>%
  top_n(20, suscide_Rate)

# Create the bar plot with ggplot
bar_plot <- ggplot(avg_suicide_rate, aes(x = reorder(Entity, suscide_Rate), 
                                           y = suscide_Rate, 
                                           fill = suscide_Rate,
                                           text = paste("Country:", Entity, "<br>Average Suicide Rate:", suscide_Rate))) +
  geom_bar(stat = "identity") +
  coord_flip() +  # Flip coordinates to make country names readable
  scale_fill_gradient(low = "#ffcccc", high = "#990000") +  # Light red to dark red gradient
  labs(title = "Suicide Rates Among 15-19 Year Olds in the Top 20 Nations (All Years)", 
       x = "Country", 
       y = "Average Suicide Rate per 100,000 Population") +
  theme(axis.text.y = element_text(angle = 0, hjust = 1, size = 8)) +  # Adjust text angle and size
  theme(plot.margin = margin(10, 10, 10, 40)) +  # Adjust margins
  guides(fill = guide_legend(title = "Suicide Rate per 100,000"))  # Change the legend title

# Convert to an interactive plot with plotly
interactive_plot <- ggplotly(bar_plot, tooltip = "text")  # Use the 'text' aesthetic for tooltip

# Show the interactive plot
interactive_plot